PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Manifold learning using robust graph Laplacian for interactive image retrieval
Hichem Sahbi, Patrick Etyngier, Jean-Yves Audibert and Renaud Keriven
In: CVPR 2008, 24-26 Jun 2008, Alaska, US.

Abstract

Interactive image search or relevance feedback is the process which helps a user refining his query and finding difficult target categories. This consists in partially labeling a very small fraction of an image database and iteratively refining a decision rule using both the labeled and unlabeled data. Training of this decision rule is referred to as transductive learning. Our work is an original approach for relevance feedback based on Graph Laplacian. We introduce a new Graph Laplacian which makes it possible to robustly learn the embedding, of the manifold enclosing the dataset, via a diffusion map. Our approach is three-folds: it allows us (i) to integrate all the unlabeled images in the decision process (ii) to robustly capture the topology of the image set and (iii) to perform the search process inside the manifold. Relevance feedback experiments were conducted on simple databases including Olivetti and Swedish as well as challenging and large scale databases including Corel. Comparisons show clear and consistent gain, of our graph Laplacian method, with respect to state-of-the art relevance feedback approaches.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:5083
Deposited By:Jean-Yves Audibert
Deposited On:24 March 2009